Illya Bakurov, José Manuel Muñoz Contreras, Mauro Castelli, Nuno Rodrigues, Sara Silva, Leonardo Trujillo, Leonardo Vanneschi
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Geometric semantic genetic programming with normalized and standardized random programs
Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the last decade. The results achieved by incorporating semantic awareness in the evolutionary process demonstrate the impact that geometric semantic operators have brought to the field of EC. An improvement to the geometric semantic mutation (GSM) operator is proposed, inspired by the results achieved by batch normalization in deep learning. While, in one of its most used versions, GSM relies on the use of the sigmoid function to constrain the semantics of two random programs responsible for perturbing the parent’s semantics, here a different approach is followed, which allows reducing the size of the resulting programs and overcoming the issues associated with the use of the sigmoid function, as commonly done in deep learning. The idea is to consider a single random program and use it to perturb the parent’s semantics only after standardization or normalization. The experimental results demonstrate the suitability of the proposed approach: despite its simplicity, the presented GSM variants outperform standard GSGP on the studied benchmarks, with a difference in terms of performance that is statistically significant. Furthermore, the individuals generated by the new GSM variants are easier to simplify, allowing us to create accurate but significantly smaller solutions.
期刊介绍:
A unique source reporting on methods for artificial evolution of programs and machines...
Reports innovative and significant progress in automatic evolution of software and hardware.
Features both theoretical and application papers.
Covers hardware implementations, artificial life, molecular computing and emergent computation techniques.
Examines such related topics as evolutionary algorithms with variable-size genomes, alternate methods of program induction, approaches to engineering systems development based on embryology, morphogenesis or other techniques inspired by adaptive natural systems.